Evaluation of serum-based cancer biomarkers: A brief review from a clinical and computational viewpoint

Identification of energy metabolism anomalies and serum biomarkers in the progression of premature ovarian failure via extracellular vesicles' proteomic and metabolomic profiles

BACKGROUND

Premature ovarian failure (POF) is a clinical condition characterized by the cessation of ovarian function, leading to infertility. The underlying molecular mechanisms remain unclear, and no predictable biomarkers have been identified. This study aimed to investigate the protein and metabolite contents of serum extracellular vesicles to investigate underlying molecular mechanisms and explore potential biomarkers.

METHODS

This study was conducted on a cohort consisting of 14 POF patients and 16 healthy controls. The extracellular vesicles extracted from the serum of each group were subjected to label-free proteomic and unbiased metabolomic analysis. Differentially expressed proteins and metabolites were annotated. Pathway network clustering was conducted with further correlation analysis. The biomarkers were confirmed by ROC analysis and random forest machine learning.

RESULTS

The proteomic and metabolomic profiles of POF patients and healthy controls were compared. Two subgroups of POF patients, Pre-POF and Pro-POF, were identified based on the proteomic profile, while all patients displayed a distinguishable metabolomic profile. Proteomic analysis suggested that inflammation serves as an early factor contributing to the infertility of POF patients. For the metabolomic analysis, despite the dysfunction of metabolism, oxidative stress and hormone imbalance were other key factors appearing in POF patients. Signaling pathway clustering of proteomic and metabolomic profiles revealed the progression of dysfunctional energy metabolism during the development of POF. Moreover, correlation analysis identified that differentially expressed proteins and metabolites were highly associated, with six of them being selected as potential biomarkers. ROC curve analysis, together with random forest machine learning, suggested that AFM combined with 2-oxoarginine was the best diagnostic biomarker for POF.

CONCLUSIONS

Omics analysis revealed that inflammation, oxidative stress, and hormone imbalance are factors that damage ovarian tissue, but the progressive dysfunction of energy metabolism might be the critical pathogenic pathway contributing to the development of POF. AFM combined with 2-oxoarginine serves as a precise biomarker for clinical POF diagnosis.

Combination of hsa_circ_0004674 and lncRNA OIP5‑AS1 as a novel clinical biomarker used to predict prognosis in patients with osteosarcoma

Osteosarcoma is a malignant tumor that predominantly occurs in children or adolescents under the age of 20 years old. Metastasis and chemotherapy resistance are two problems in the treatment of osteosarcoma, and the lack of definite biomarkers impairs the course of treatment. In recent years, non-coding RNA, as a biomarker of osteosarcoma, has become an area of research focus. The role of long non-coding RNAs (lncRNAs), such as lncRNA OIP5-AS1, and circular RNAs, such as hsa_circ_0004674, in osteosarcoma have previously been revealed, and the present study investigated their clinical significance. A total of 20 samples were collected from patients with osteosarcoma. The expression levels of lncRNA OIP5-AS1 and hsa_circ_0004674 were analyzed in tumor tissues and patient serum, and their associations with chemotherapy sensitivity, lung metastasis and prognosis were assessed. The results revealed that these two non-coding RNAs were significantly upregulated in the osteosarcoma tissues of patients compared with those in the adjacent tumor tissues. In addition, the expression levels of the two non-coding RNAs were increased in the serum of patients with osteosarcoma compared with those in patients with bone fractures (P<0.01). In patients with lung metastasis or chemotherapy resistance (tumor necrosis rate <90%), the expression levels of the two non-coding RNAs were similarly increased. By plotting the receiver operating characteristic curve, it was revealed that the combination of hsa_circ_0004674 and lncRNA OIP5-AS1 was better than ALP or either non-coding RNA alone in predicting chemotherapy sensitivity and metastasis. Kaplan-Meier survival analysis showed that, in patients with osteosarcoma, higher expression of both non-coding RNAs was associated with worse survival time (log-rank test P=0.006). In conclusion, the combination of hsa_circ_0004674 and lncRNA OIP5-AS1 may be used as a better biomarker than traditional biomarkers, such as ALP, in a clinical setting.

Exact Probability Distribution for the ROC Area under Curve

The Receiver Operating Characteristic (ROC) is a de facto standard for determining the accuracy of in vitro diagnostic (IVD) medical devices, and thus the exactness in its probability distribution is crucial toward accurate statistical inference. We show the exact probability distribution of the ROC AUC-value, hence exact critical values and p-values are readily obtained. Because the exact calculations are computationally intense, we demonstrate a method of geometric interpolation, which is exact in a special case but generally an approximation, vastly increasing computational speeds. The method is illustrated through open access data, demonstrating superiority of 26 composite biomarkers relative to a predicate device. Especially under correction for testing of multiple hypotheses, traditional asymptotic approximations are encumbered by considerable imprecision, adversely affecting IVD device development. The ability to obtain exact p-values will allow more efficient IVD device development.

Evaluation of the relationship between prestin serum biomarker and sensorineural hearing loss: a case-control study

RESEARCH BACKGROUND AND AIM

There is not any routine serum biomarker for diagnosing hearing loss (HL). An inner ear-specific protein, prestin can be measured as a serum biochemical marker for HL diagnosis. The present study investigates, for the first time, the relationship between prestin serum levels and sensorineural HL (SNHL) in an Iranian population.

MATERIALS AND METHODS

In this case-control study, 176 samples were examined in four groups including two control and two SNHL groups of 20-50 and ≥ 50 years with different severities of SNHL. Plasma prestin concentration was measured using Human Prestin (SLC26A5) ELISA Kit. Data analysis was conducted using SPSS v.23 with level of significance as 0.05.

RESULTS

Groups with SNHL had higher prestin levels (Mean = 182.29, SD = 71.24) compared to the control groups (Mean = 122.50, SD = 57.1) (P < 0.001). Results of the multinomial logistic regression of relationship between prestin level and SNHL remained significant after controlling intervening variables (P < 0.001 and odds ratio = 1.017 and 95% CI OR: 1.01-1.024). Results of the ordinal logistic regression model revealed that prestin level was significantly associated with the degree of HL (P < 0.001 and Odds ratio = 1.009 and 95% CI and OR: 1.005-1.013), so that the likelihood of HL increased with the rise in prestin levels. The best cutoff point for the 20-50 group was the prestin content of 132.5 pg/ml (sensitivity: 75%, specificity: 70.05%), while for the group of ≥ 50 was as 130 pg/ml (sensitivity: 84.1%, specificity: 68.2%).

CONCLUSIONS

Results of the present study revealed that prestin acts as a valuable biomarker for SNHL.

The role of miRNAs and lncRNAs in neurofibromatosis type 1

Neurofibromatosis Type 1 (NF1) is a frequent cancer predisposition syndrome. The common hallmark of patients with this multisystemic genetic disorder is the formation of peripheral nerve sheath tumors, which can be seen as either dermal, plexiform, and malignant forms. MicroRNA (miRNA) is an essential gene regulation factor and consists of 22-25 nucleotides. MiRNAs are identified to act as both tumor suppressors and oncogenes (oncomirs) in a wide variety of human cancers. They play multiple roles in molecular pathways responsible for tumor homing, progression, and invasion. Long noncoding RNA (lncRNA) also has a key role in cancer transcriptomics. Altered lncRNA expression levels have been found in various malignancies. This review aims to summarize the role of two noncoding RNA groups, miRNAs and lncRNAs, in NF1 establishment, development, and progression. We also highlight their potential for future clinical interventions and devising new diagnostic tools.

The use of interleukin-6 as a biomarker of lung cancer: A systematic review and meta-analysis

BACKGROUND

Lung cancer is known for its fatality due to diagnosis at a late stage, indicating the need for the discovery of novel diagnostic biomarkers. Interleukin-6 (IL-6) belongs to a family of inflammatory cytokines shown to be elevated in cancer patients. Hence, in this study, a systematic review and meta-analysis was undertaken to evaluate the association of IL-6 levels between lung cancer patients and healthy individuals, as this would further support its use as a clinical biomarker.

METHODS

All major electronic databases were systematically searched to find the existing literature from 2012 until September 2022 on the association of IL-6 levels with lung cancer. Mean and standard deviation of IL-6 levels of lung cancer patients and controls were recorded from the included case-control studies. The natural logarithm of the ratio of means (RoM) between patients and controls with its respective 95% confidence intervals was calculated to retrieve a pooled RoM value.

RESULTS

Eight studies involving 559 lung cancer patients and 462 healthy controls were in included in the meta-analysis and a random-effects model was used due to high heterogeneity (I2 = 99.38%). Overall, IL-6 was found to be higher in lung cancer patients (pooled ln RoM = 1.20, 95% CI: 0.72-1.69, P < 0.0001) and all included studies were found to carry a low risk of bias after quality assessment.

CONCLUSIONS

This meta-analysis revealed that IL-6 levels are higher in biological samples of lung cancer patients, indicating that they could be used as a biomarker for diagnosing lung cancer without complications. Further research should be undertaken to evaluate its diagnostic accuracy, in order to obtain more concrete evidence for its clinical use.

Role of Long Non-Coding RNA LINC00641 in Cancer

Long non-coding RNAs (lncRNAs) are non-protein coding RNAs with more than 200 nucleic acids in length. When lncRNAs are located in the nucleus, they regulate chromosome structure, participate in chromatin remodeling, and act as transcription regulators. When lncRNAs are exported to the cytoplasm, they regulate mRNA stability, regulate translation, and interfere with post-translational modification. In recent years, more and more evidences have shown that lncRNA can regulate the biological processes of tumor proliferation, apoptosis, invasion and metastasis, and can participate in a variety of tumor signaling pathways. Long-gene non-protein coding RNA641 (LINC00641), located on human chromosome 14q11.2, is differentially expressed in a variety of tumors and is related to overall survival and prognosis, etc. Interfering the expression of LINC00641 can lead to changes in tumor cell proliferation, invasion, metastasis, apoptosis and other biological behaviors. Therefore, LINC00641 is a promising new biomarker and potential clinical therapeutic target. In this review, the biological functions, related mechanisms and clinical significance of LINC00641 in many human cancers are described in detail.

Quantitative predictive approaches for Dupuytren disease: a brief review and future perspectives

In this study we review the current state of the art for Dupuytren's disease (DD), while emphasising the need for a better integration of clinical, experimental and quantitative predictive approaches to understand the evolution of the disease and improve current treatments. We start with a brief review of the biology of this disease and current treatment approaches. Then, since certain aspects in the pathogenesis of this disorder have been compared to various biological aspects of wound healing and malignant processes, next we review some in silico (mathematical modelling and simulations) predictive approaches for complex multi-scale biological interactions occurring in wound healing and cancer. We also review the very few in silico approaches for DD, and emphasise the applicability of these approaches to address more biological questions related to this disease. We conclude by proposing new mathematical modelling and computational approaches for DD, which could be used in the absence of animal models to make qualitative and quantitative predictions about the evolution of this disease that could be further tested in vitro.

Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

Background

Osteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis.

Methods

We performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways.

Results

Eighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein–protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein–protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial–mesenchymal transition.

Conclusion

We identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02308-w.

Significantly higher serum tumor marker levels in patients with oral submucous fibrosis

Background/purpose

Our previous study showed that carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC-Ag), and ferritin are significantly higher in patients with oral potentially malignant disorders (OPMDs including oral leukoplakia, oral erythroleukoplakia, and oral verrucous hyperplasia) than in healthy controls (HCs). Oral submucous fibrosis (OSF) is also recognized as an OPMD. This study evaluated whether these three serum tumor marker levels were also significantly higher in OSF patients than in HCs.

Materials and methods

The serum CEA, SCC-Ag, and ferritin levels in 41 OSF patients and 164 HCs were measured and compared. Patients with serum CEA level ≥3 ng/mL, SCC-Ag level ≥2 ng/mL, and ferritin level ≥250 ng/mL were scored as serum positive for CEA, SCC-Ag, and ferritin, respectively.

Results

We found significantly higher mean serum CEA, SCC-Ag, and ferritin levels in 41 OSF patients than in 164 HCs (all P-values < 0.05). Moreover, 41 OSF patients had significantly higher serum positive rates of CEA (39.0%), SCC-Ag (19.5%), and ferritin (53.7%) than 164 HCs (all P-values < 0.05). Of the 41 OSF patients, 26 (63.4%), 7 (17.1%), and 2 (4.9%) had serum positivities of one, two, or three tumor markers including CEA, SCC-Ag, and ferritin, respectively.

Conclusion

There are significantly higher mean serum CEA, SCC-Ag, and ferritin levels and significantly higher serum positive rates of CEA, SCC-Ag, and ferritin in OSF patients than in HCs. The serum CEA, SCC-Ag, and ferritin levels may be served as tumor markers for evaluation of malignant potential of OSF lesions.

Proinflammatory cytokines and colorectal cancer - the impact of the stage

Colorectal cancer is one of the most often diagnosed malignant tumors. In Kazakhstan, high incidence of CC is registered along with other oncology diseases. Despite a significant progress in the disease treatment achieved lately, CC is still one of the major reasons of mortality due to oncologic pathologies.

To study the samples MilliplexMap HumanCirculationBiomarker panel in blood serum was used. XMap-based Fluorescence immunoassay was implemented, which comprised magnetic-bead-based simultaneous fluorescence detection of IL-6, IL-8, MIF, FGF-2, SCF, TGF, TNF, TRAIL analytes. Proinflammatory biomarker concentration detection at different CC stages allows to reveal the dynamics of inflammatory response of the organism to tumor and to use them (biomarkers) in further diagnostic and forecast in particular in CC. As a result of our study, it was found that IL-6, which showed the brightest reaction, due to its range of change and considerable shift already in the I stage can be recommended as a component of a complex diagnostic panel. Such markers as FGF2 and MIF also have a role in CC early stage detection.

Prognostic Value of Preoperative Serum Calcitonin Levels for Predicting the Recurrence of Medullary Thyroid Carcinoma

BACKGROUND

Serum calcitonin level is a useful biomarker for predicting primary tumor size, the extent of lymph node, and distant metastasis in patients with medullary thyroid carcinoma (MTC). However, the association between preoperative serum calcitonin levels and long-term oncologic outcomes has not yet been established. The aims of this study were to determine the preoperative serum calcitonin cut-off value for predicting disease recurrence and to evaluate its prognostic value.

METHODS

Patients with MTC (n = 169) who were treated at a tertiary referral hospital in Korea between 1995 and 2019 were enrolled. To determine the preoperative serum calcitonin cut-off value for predicting structural recurrence, the maximum of the standardized log-rank statistics of all possible cut-off values was used. Multivariable Cox regression analysis was used to determine prognostic factors for disease-free survival.

RESULTS

The overall disease-free survival rate was 75.7%. The preoperative serum calcitonin cut-off value that predicted structural recurrence was 309 pg/mL. Preoperative serum calcitonin levels of > 309 pg/mL were the strongest independent predictor of disease recurrence (hazard ratio (HR) 5.33, 95% confidence interval (85% CI) 1.67-16.96; P = 0.005). Lateral lymph node metastasis (HR 3.70, 95% CI 1.61-8.51; P = 0.002) and positive resection margins (HR 3.57, 95% CI 1.44-8.88; P = 0.006) were also significant predictors of disease recurrence.

CONCLUSIONS

The preoperative serum calcitonin cut-off value is useful in clinical practice. It is also the best predictive factor for disease-free survival. Preoperative serum calcitonin levels may help determine the optimal postoperative follow-up strategy for patients with MTC.

Multicompartment modeling of protein shedding kinetics during vascularized tumor growth

Identification of protein biomarkers for cancer diagnosis and prognosis remains a critical unmet clinical need. A major reason is that the dynamic relationship between proliferating and necrotic cell populations during vascularized tumor growth, and the associated extra- and intra-cellular protein outflux from these populations into blood circulation remains poorly understood. Complementary to experimental efforts, mathematical approaches have been employed to effectively simulate the kinetics of detectable surface proteins (e.g., CA-125) shed into the bloodstream. However, existing models can be difficult to tune and may be unable to capture the dynamics of non-extracellular proteins, such as those shed from necrotic and apoptosing cells. The models may also fail to account for intra-tumoral spatial and microenvironmental heterogeneity. We present a new multi-compartment model to simulate heterogeneously vascularized growing tumors and the corresponding protein outflux. Model parameters can be tuned from histology data, including relative vascular volume, mean vessel diameter, and distance from vasculature to necrotic tissue. The model enables evaluating the difference in shedding rates between extra- and non-extracellular proteins from viable and necrosing cells as a function of heterogeneous vascularization. Simulation results indicate that under certain conditions it is possible for non-extracellular proteins to have superior outflux relative to extracellular proteins. This work contributes towards the goal of cancer biomarker identification by enabling simulation of protein shedding kinetics based on tumor tissue-specific characteristics. Ultimately, we anticipate that models like the one introduced herein will enable examining origins and circulating dynamics of candidate biomarkers, thus facilitating marker selection for validation studies.

An enhanced centrifugation-assisted lateral flow immunoassay for the point-of-care detection of protein biomarkers

Protein biomarkers are widely used for disease diagnosis, but the current detection methods utilized in centralized laboratories are mainly based on enzyme-linked immunosorbent assay (ELISA)-derived sandwich-type immunoassays such as chemiluminescent or electrochemiluminescent immunoassays, which suffer from long detection times and cumbersome instruments. For the point-of-care (POC) detection of protein biomarkers, various test strips for lateral flow immunoassay (LFIA) have been manufactured, but their detection sensitivities and capabilities for raw samples are limited. In this study, an enhanced centrifugation-assisted lateral flow immunoassay (ECLFIA) was established to rapidly detect protein biomarkers in whole blood with a higher sensitivity than LFIA. By inserting a nitrocellulose membrane into a centrifugal disc, fully automated operations, including sample preparation, active lateral flow actuation, washing, and signal amplification, which could hardly be performed in conventional LFIA, were enabled on the centrifugal platform for ECLFIA. The entire process for detecting human prostate specific antigen (PSA) in a drop of blood (20 μL) could be completed in 15 min. The limit of detection for our ECLFIA system was 0.028 ng mL^-1, showing a 21.4-fold improvement compared to that of LFIA. Moreover, this system was utilized to detect PSA in 34 clinical samples. The results were compared to those measured using a commercial instrument used in the hospital, and a good correlation coefficient of 0.986 was obtained, demonstrating the practicality of this ECLFIA system. In summary, the ECLFIA system established in this study can be an efficient tool for the POC detection of protein biomarkers with comprehensive advantages in sensitivity, simplicity and speed.

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

Background

Studies on tumor-secreted microRNAs point to a functional role of these in cellular communication and reprogramming of the tumor microenvironment. Uptake of tumor-secreted microRNAs by neighboring cells may result in the silencing of mRNA targets and, in turn, modulation of the transcriptome. Studying miRNAs externalized from tumors could improve cancer patient diagnosis and disease monitoring and help to pinpoint which miRNA-gene interactions are central for tumor properties such as invasiveness and metastasis.

Methods

Using a bioinformatics approach, we analyzed the profiles of secreted tumor and normal interstitial fluid (IF) microRNAs, from women with breast cancer (BC). We carried out differential abundance analysis (DAA), to obtain miRNAs, which were enriched or depleted in IFs, from patients with different clinical traits. Subsequently, miRNA family enrichment analysis was performed to assess whether any families were over-represented in the specific sets. We identified dysregulated genes in tumor tissues from the same cohort of patients and constructed weighted gene co-expression networks, to extract sets of co-expressed genes and co-abundant miRNAs. Lastly, we integrated miRNAs and mRNAs to obtain interaction networks and supported our findings using prediction tools and cancer gene databases.

Results

Network analysis showed co-expressed genes and miRNA regulators, associated with tumor lymphocyte infiltration. All of the genes were involved in immune system processes, and many had previously been associated with cancer immunity. A subset of these, BTLA, CXCL13, IL7R, LAMP3, and LTB, was linked to the presence of tertiary lymphoid structures and high endothelial venules within tumors. Co-abundant tumor interstitial fluid miRNAs within this network, including miR-146a and miR-494, were annotated as negative regulators of immune-stimulatory responses. One co-expression network encompassed differences between BC subtypes. Genes differentially co-expressed between luminal B and triple-negative breast cancer (TNBC) were connected with sphingolipid metabolism and predicted to be co-regulated by miR-23a. Co-expressed genes and TIF miRNAs associated with tumor grade were BTRC, CHST1, miR-10a/b, miR-107, miR-301a, and miR-454.

Conclusion

Integration of IF miRNAs and mRNAs unveiled networks associated with patient clinicopathological traits, and underlined molecular mechanisms, specific to BC sub-groups. Our results highlight the benefits of an integrative approach to biomarker discovery, placing secreted miRNAs within a biological context.

Magnetic Bead-Immobilized Mammalian Cells Are Effective Targets to Enrich Ligand-Displaying Yeast

Yeast surface display empowers selection of protein binding ligands, typically using recombinant soluble antigens. However, ectodomain fragments of transmembrane targets may fail to recapitulate their true, membrane-bound form. Direct selections against adhered mammalian cells empower enrichment of genuine binders yet benefit from high target expression, robustly adherent mammalian cells, and nanomolar affinity ligands. This study evaluates a modified format with mammalian cells immobilized to magnetic beads; yeast-displayed fibronectin domain and affibody ligands of known affinities and cells with expression ranges of epidermal growth factor receptor (EGFR) and CD276 elucidate important parameters to ligand enrichment and yield in cell suspension panning with comparison to adherent panning. Cell suspension panning is hindered by significant background of nondisplaying yeast but exhibits yield advantages in model EGFR systems for a high affinity (K_D = 2 nM) binder on cells with both high (10⁶ per cell) target expression (9.6 ± 0.6% vs 3.2 ± 0.4%, p < 0.0001) and mid (10⁵) target expression (2.3 ± 0.5% vs 0.41 ± 0.09%, p = 0.0008), as well as for a low affinity (K_D > 600 nM) binder on high target expression cells (2.0 ± 0.5% vs 0.017 ± 0.005%; p = 0.001). Significant enrichment was observed for all EGFR systems except the low-affinity, high expression system. The CD276 system failed to provide significant enrichment, indicating that this technique may not be suitable for all targets. Collectively, this study highlights new approaches that yield successful enrichment of yeast-displayed ligands via panning on immobilized mammalian cells.

CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data

With the improvement of -omics and next-generation sequencing (NGS) methodologies, along with the lowered cost of generating these types of data, the analysis of high-throughput biological data has become standard both for forming and testing biomedical hypotheses. Our knowledge of how to normalize datasets to remove latent undesirable variances has grown extensively, making for standardized data that are easily compared between studies. Here we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper (https://github.com/ELELAB/CAncer-bioMarker-Prediction-Pipeline -CAMPP) intended to aid bioinformatic software-users with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. To avoid issues relating to R-package updates, a renv .lock file is provided to ensure R-package stability. Data-management includes missing value imputation, data normalization, and distributional checks. CAMPP performs (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis, and (VI) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist in streamlining bioinformatic analysis of quantitative biological data, whilst ensuring an appropriate bio-statistical framework.

Ligand Engineering via Yeast Surface Display and Adherent Cell Panning

High-throughput ligand discovery and evolution-via genotype-phenotype linkage strategies-empower molecularly targeted therapy, diagnostics, and fundamental science. Maintaining high-quality target antigen in these selections, particularly for membrane targets, is often a technical challenge. Panning yeast-displayed ligand libraries on intact mammalian cells expressing the molecular target has emerged as an effective strategy. Herein we describe the techniques used to select target-binding ligands via this approach including the use of target-negative cells to deplete non-specific binders and avidity reduction to preferentially select high-affinity ligands.

Lectin biosensors in cancer glycan biomarker detection

Cancer has high incidence and it will continue to increase over the next decades. Detection and quantification of cancer-associated biomarkers is frequently carried out for diagnosis, prognosis and treatment monitoring at various disease stages. It is well-known that glycosylation profiles change significantly during oncogenesis. Aberrant glycans produced during tumorigenesis are, therefore, valuable molecules for detection and characterization of cancer, and for therapeutic design and monitoring. Although glycoproteomics has benefited from the development of analytical tools such as high performance liquid chromatography, two-dimensional gel and capillary electrophoresis and mass spectrometry, these approaches are not well suited for rapid point-of-care (POC) testing easily performed by medical staff. Lectins are biomolecules found in nature with specific affinities toward particular glycan structures and bind them thus forming a relatively strong complex. Because of this characteristic, lectins have been used in analytical techniques for the selective capture or separation of certain glycans in complex samples, namely, in lectin affinity chromatography, or to characterize glycosylation profiles in diverse clinical situations, using lectin microarrays. Lectin-based biosensors have been developed for the detection of specific aberrant and cancer-associated glycostructures to aid diagnosis, prognosis and treatment assessment of these patients. The attractive features of biosensors, such as portability and simple use make them highly suitable for POC testing. Recent developments in lectin biosensors, as well as their potential and pitfalls in cancer glycan biomarker detection, are presented in this chapter.

A Preliminary Report on the Investigation of Prestin as a Biomarker for Idiopathic Sudden Sensorineural Hearing Loss

To date, no specific biomarkers for idiopathic sudden sensorineural hearing loss (ISSHL) have been used. The aim of this study is to investigate whether prestin, the motor protein of cochlear outer hair cells, could be used as a biomarker candidate for the diagnosis and prognosis judgement of ISSHL. Blood samples of 14 ISSHL patients and 28 control patients without history of hearing loss were collected. Plasma prestin concentration was measured using Human Prestin (SLC26A5) ELISA Kit. The results showed that prestin was detectable in the plasma of all patients and the concentration of prestin was significantly higher in ISSHL patients with about half being above the average range of control patients. Moreover, in treatment responsive group, 6 of 10 patients had decreased prestin levels after treatment compared to those of before treatment, while the prestin levels of all the 4 patients in treatment unresponsive group increased in varying degrees. Our promising preliminary results suggest that prestin has the potential to serve as a biomarker to assist diagnosis and judge response to pharmacological treatments.

Evaluation of the prognostic utility of the combination of platelet count with mean platelet volume as a prognostic indicator in head and neck cancer

The combination of platelet count to mean platelet volume (COP-MPV) has been recently reported as a prognostic indicator of oral cavity cancer and other cancer sites. The aim of the present study was to validate the utility of the COP-MPV as a prognostic indicator in all head and neck cancer (HNC) sites. The clinicopathological characteristics of the COP-MPV with HNC were also investigated. This is a retrospective cohort study that recruited consecutively treated patients at a tertiary level academic hospital. Clinicopathological characteristics were recorded, including the COP-MPV scores. Survival was analyzed using Kaplan-Meier analysis, as well as multivariate Cox Proportional Hazards regression. COP-MPV was not associated with the survival outcome in univariate or multivariate analysis. In the multivariate model, tumor differentiation, tumor stage, nodal stage, surgical margins and hemoglobin were revealed to be significantly associated with survival. The results demonstrated that the COP-MPV is not a suitable prognostic factor for HNC.

Identification of diagnostic biomarker in patients with gestational diabetes mellitus based on transcriptome-wide gene expression and pattern recognition

Gestational diabetes mellitus (GDM) is becoming a growing threat for all pregnancies. In this study, we set up an automatic screening method combining both transcriptomic databases and support vector machine (SVM)-based pattern recognition to select biomarkers that can be used in predicting and preventing GDM for gravidas. We screened 63 samples (32 GDM samples and 31 normal controls) in GEO database for the GDM-specific biomarkers. Differentially expressed genes between patients with GDM and normal controls were picked out using edgeR package. Enrichment analysis was performed using database for annotation, visualization, and integrated discovery. The regulatory gene network was constructed based on the KEGG pathway database. Genes in the hub of the network were selected as specific biomarkers of GDM and further validated through document investigation. Finally, the GDM prediction model was verified using the SVMs. In total, 189 probes corresponding to 69 genes that differentially expressed between GDM and controls were screened out by edgeR package. Nineteen pathways were clustered by KEGG enrichment analysis and were integrated into a regulatory network containing 572 nodes and 1874 edges. The intersection of 50 hub genes extracted from the network and 69 differential genes picked out by edgeR was a collection of six genes, including members of HLA superfamily. In the SVM model, the six genes had a good capacity of predicting GDM in both the training data set (area under curve [AUC] is 0.781) and the testing data set (AUC is 0.710) and had been reported to be associated with GDM. We found that the collection of six genes can be potentially applied as a biomarker for GDM diagnosis.

Lectin-based biosensors as analytical tools for clinical oncology

The review focus on the use of lectin-based biosensors in the oncology field, and ponders the potentialities of using these devices as analytical tools to monitor the levels of cancer glycobiomarkers in biological fluids, helping in the diagnosis, prognosis and treatment assessment. Several examples of lectin-based biosensors directed for cancer biomarkers are described and discussed, and their potential application in the clinic is considered, taking into account their analytical features, advantages and performance in sample analysis. Technical and practical aspects in the construction process, which are specific for lectin biosensors, are debated, as well as the requirements in sample collection and processing, and biosensor validation. Today's challenges for real implementation of these devices in the clinic are presented, along with the future trends in the field.

Automated extraction of Biomarker information from pathology reports

Background

Pathology reports are written in free-text form, which precludes efficient data gathering. We aimed to overcome this limitation and design an automated system for extracting biomarker profiles from accumulated pathology reports.

Methods

We designed a new data model for representing biomarker knowledge. The automated system parses immunohistochemistry reports based on a “slide paragraph” unit defined as a set of immunohistochemistry findings obtained for the same tissue slide. Pathology reports are parsed using context-free grammar for immunohistochemistry, and using a tree-like structure for surgical pathology. The performance of the approach was validated on manually annotated pathology reports of 100 randomly selected patients managed at Seoul National University Hospital.

Results

High F-scores were obtained for parsing biomarker name and corresponding test results (0.999 and 0.998, respectively) from the immunohistochemistry reports, compared to relatively poor performance for parsing surgical pathology findings. However, applying the proposed approach to our single-center dataset revealed information on 221 unique biomarkers, which represents a richer result than biomarker profiles obtained based on the published literature. Owing to the data representation model, the proposed approach can associate biomarker profiles extracted from an immunohistochemistry report with corresponding pathology findings listed in one or more surgical pathology reports. Term variations are resolved by normalization to corresponding preferred terms determined by expanded dictionary look-up and text similarity-based search.

Conclusions

Our proposed approach for biomarker data extraction addresses key limitations regarding data representation and can handle reports prepared in the clinical setting, which often contain incomplete sentences, typographical errors, and inconsistent formatting.

Electronic supplementary material

The online version of this article (10.1186/s12911-018-0609-7) contains supplementary material, which is available to authorized users.

Count of platelet and mean platelet volume score: serologic prognostic factor in patients with oral squamous cell carcinoma

Objectives

TNM staging, especially for lymph node metastasis, is the scoring system most widely used among prognostic factors for cancer survival. Several biomarkers have been studied as serologic markers, but their specificity is low and clinical applications are difficult. This study aimed to establish a scoring system for patients with oral squamous cell carcinoma (OSCC) using platelet (PLT) and mean platelet volume (MPV) levels measured postoperatively and to evaluate their significance as prognostic factors.

Materials and Methods

We studied 40 patients admitted to the Department of Oral and Maxillofacial Surgery of Dankook University Hospital who were diagnosed with primary OSCC histopathologically between May 2006 and May 2012. Clinical pathological information obtained from the medical records of each patient included age, sex, height, weight, tumor location, degree of differentiation, tumor diameter, lymph node metastasis, TNM stage, and other test values including white blood cell, MPV, PLT, C-reactive protein (CRP), and albumin obtained through a test conducted within 7 days before surgery. Count of platelet (COP)-MPV Score: Patients with both PLT and MPV values below the cut-off values were defined as score 0 (group A). Patients with at least one of the two higher than the cut-off value were defined as score 1 (group B).

Results

Univariate analyses showed N-metastasis, COP-MPV (A vs B), PLT, platelet-lymphocyte ratio, and CRP were statistically significant prognostic factors. A multivariate Cox proportional hazards model showed N-metastasis (hazard ratio [HR] 6.227, P=0.016) and COP-MPV (A vs B) (HR 18.992, P=0.013) were independent prognostic factors with a significant effect on survival.

Conclusion

COP-MPV score is a simple and cost-effective test method and is considered a more effective prognostic factor than other considered factors in predicting the prognosis of OSCC patients.

CANcer-specific Evaluation System (CANES): a high-accuracy platform, for preclinical single/multi-biomarker discovery

The recent creation of enormous, cancer-related “Big Data” public depositories represents a powerful means for understanding tumorigenesis. However, a consistently accurate system for clinically evaluating single/multi-biomarkers remains lacking, and it has been asserted that oft-failed clinical advancement of biomarkers occurs within the very early stages of biomarker assessment. To address these challenges, we developed a clinically testable, web-based tool, CANcer-specific single/multi-biomarker Evaluation System (CANES), to evaluate biomarker effectiveness, across 2,134 whole transcriptome datasets, from 94,147 biological samples (from 18 tumor types). For user-provided single/multi-biomarkers, CANES evaluates the performance of single/multi-biomarker candidates, based on four classification methods, support vector machine, random forest, neural networks, and classification and regression trees. In addition, CANES offers several advantages over earlier analysis tools, including: 1) survival analysis; 2) evaluation of mature miRNAs as markers for user-defined diagnostic or prognostic purposes; and 3) provision of a “pan-cancer” summary view, based on each single marker. We believe that such “landscape” evaluation of single/multi-biomarkers, for diagnostic therapeutic/prognostic decision-making, will be highly valuable for the discovery and “repurposing” of existing biomarkers (and their specific targeted therapies), leading to improved patient therapeutic stratification, a key component of targeted therapy success for the avoidance of therapy resistance.

Integrated electrokinetically driven microfluidic devices with pH-mediated solid-phase extraction coupled to microchip electrophoresis for preterm birth biomarkers

Integration in microfluidics is important for achieving automation. Sample preconcentration integrated with separation in a microfluidic setup can have a substantial impact on rapid analysis of low-abundance disease biomarkers. Here, we have developed a microfluidic device that uses pH-mediated solid-phase extraction (SPE) for the enrichment and elution of preterm birth (PTB) biomarkers. Furthermore, this SPE module was integrated with microchip electrophoresis for combined enrichment and separation of multiple analytes, including a PTB peptide biomarker (P1). A reversed-phase octyl methacrylate monolith was polymerized as the SPE medium in polyethylene glycol diacrylate modified cyclic olefin copolymer microfluidic channels. Eluent for pH-mediated SPE of PTB biomarkers on the monolith was optimized using different pH values and ionic concentrations. Nearly 50-fold enrichment was observed in single channel SPE devices for a low nanomolar solution of P1, with great elution time reproducibility (<7% RSD). The monolith binding capacity was determined to be 400 pg (0.2 pmol). A mixture of a model peptide (FA) and a PTB biomarker (P1) was extracted, eluted, injected, and then separated by microchip electrophoresis in our integrated device with ∼15-fold enrichment. This device shows important progress towards an integrated electrokinetically operated platform for preconcentration and separation of biomarkers.

Ultra-filtration of human serum for improved quantitative analysis of low molecular weight biomarkers using ATR-IR spectroscopy

Monitoring of changes in the concentrations of the low molecular weight constituents enhanced by abundant proteins depletion.

Geometry and expression enhance enrichment of functional yeast-displayed ligands via cell panning

Yeast surface display has proven to be an effective tool in the discovery and evolution of ligands with new or improved binding activity. Selections for binding activity are generally carried out using immobilized or fluorescently labeled soluble domains of target molecules such as recombinant ectodomain fragments. While this method typically provides ligands with high affinity and specificity for the soluble molecular target, translation to binding true membrane-bound cellular target is commonly problematic. Direct selections against mammalian cell surfaces can be carried out either exclusively or in combination with soluble target-based selections to further direct towards ligands for genuine cellular target. Using a series of fibronectin domain, affibody, and Gp2 ligands and human cell lines expressing a range of their targets, epidermal growth factor receptor and carcinoembryonic antigen, this study quantitatively identifies the elements that dictate ligand enrichment and yield. Most notably, extended flexible linkers between ligand and yeast enhance enrichment ratios from 1.4 ± 0.8 to 62 ± 57 for a low-affinity (>600 nM) binder on cells with high target expression and from 14 ± 13 to 74 ± 25 for a high-affinity binder (2 nM) on cells with medium valency. Inversion of the yeast display fusion from C-terminal display to N-terminal display still enables enrichment albeit with 40-97% reduced efficacy. Collectively, this study further enlightens the conditions-while highlighting new approaches-that yield successful enrichment of yeast-displayed binding ligands via panning on mammalian cells. Biotechnol. Bioeng. 2016;113: 2328-2341. © 2016 Wiley Periodicals, Inc.

Evaluation of serum amino acid profiles' utility in non-small cell lung cancer detection in Polish population

OBJECTIVES

Data from studies performed in Japanese and Korean populations suggest that free amino acid profiles have the potential to aid in non-small cell lung cancer (NSCLC) detection. However, there is still no data regarding abnormalities of free amino acids and their usefulness in NSCLC detection in European populations. The aim of the study was an evaluation of utility of amino acid profiles in NSCLC detection in Polish patients.

MATERIALS AND METHODS

Levels of 31 free amino acids were determined in 153 serum samples applying a liquid chromatography-tandem mass spectrometry-based methodology. Patients with I stage lung cancer represented a significant part of the studied group (46.7%). The obtained metabolite profiles along with clinical data were subjected to multivariate statistical tests.

RESULTS

The presented study indicated that the increased serum level of phenylalanine and decreased level of citrulline are among the most robust cancer signatures in blood of NSCLC group. In addition, increased levels of aspartic acid and β-alanine were also recognized as important features of NSCLC. Amino acid selected based on studies of Asian patients were found to have insufficient specificity in NSCLC detection in the studied population. Therefore, we proposed a new set of 6 amino acids (aspartic acid, β-alanine, histidine, asparagine, phenylalanine and serine), which ensured higher accuracy in sample classification (from 90.3% to 77.1% depending of histological type).

CONCLUSION

We indicated that some of the free amino acid alterations occur in serum of NSCLC patients in early stage of disease and thus they can be valuable components of a blood multi-marker panel for NSCLC detection.

Comprehensive and quantitative proteomic analyses of zebrafish plasma reveals conserved protein profiles between genders and between zebrafish and human

Omic approaches have been increasingly used in the zebrafish model for holistic understanding of molecular events and mechanisms of tissue functions. However, plasma is rarely used for omic profiling because of the technical challenges in collecting sufficient blood. In this study, we employed two mass spectrometric (MS) approaches for a comprehensive characterization of zebrafish plasma proteome, i.e. conventional shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) for an overview study and quantitative SWATH (Sequential Window Acquisition of all THeoretical fragment-ion spectra) for comparison between genders. 959 proteins were identified in the shotgun profiling with estimated concentrations spanning almost five orders of magnitudes. Other than the presence of a few highly abundant female egg yolk precursor proteins (vitellogenins), the proteomic profiles of male and female plasmas were very similar in both number and abundance and there were basically no other highly gender-biased proteins. The types of plasma proteins based on IPA (Ingenuity Pathway Analysis) classification and tissue sources of production were also very similar. Furthermore, the zebrafish plasma proteome shares significant similarities with human plasma proteome, in particular in top abundant proteins including apolipoproteins and complements. Thus, the current study provided a valuable dataset for future evaluation of plasma proteins in zebrafish.

Diagnostic accuracy of serum biomarkers for head and neck cancer: A systematic review and meta-analysis

Serum biomarkers could be helpful to characterize head and neck squamous cell carcinoma (HNSCC). Thus, the purpose of this systematic review and meta-analysis was to determine the diagnostic capability of serum biomarkers in the assessment of HNSCC patients. Studies were gathered by searching LILACS, PubMed, Science Direct, Scopus and Web of Science up to April 10th, 2015. Studies that focused on serum biomarkers in the diagnosis of HNSCC compared with controls were considered. Sixty-five studies were identified, and the sample size included 9098 subjects. Combined biomarkers demonstrated improved accuracy than those tested individually. Therefore, 12.8% of single and 34.3% of combined indicated that serum biomarkers discriminate patients with HNSCC from controls. The combined biomarkers with better diagnostic capability included Epidermal growth factor receptor (EGFR)+Cyclin D1 and squamous cell cancer-associated antigen (SCCA)+EGFR+Cyclin D1. Beta2-microglobin may also be a promising single biomarker for future studies. Serum biomarkers can be potentially useful in the diagnosis of HNSCC. However, further research is required to validate these biomarkers.

Tumor-Associated CSF MicroRNAs for the Prediction and Evaluation of CNS Malignancies

Cerebrospinal fluid (CSF) is a readily reachable body fluid that is reflective of the underlying pathological state of the central nervous system (CNS). Hence it has been targeted for biomarker discovery for a variety of neurological disorders. CSF is also the major route for seeding metastases of CNS malignancies and its analysis could be informative for diagnosis and risk stratification of brain cancers. Recently, modern high-throughput, microRNAs (miRNAs) measuring technology has enabled sensitive detection of distinct miRNAs that are bio-chemicallystable in the CSF and can distinguish between different types of CNS cancers. Owing to the fact that a CSF specimen can be obtained with relative ease, analysis of CSF miRNAs could be a promising contribution to clinical practice. In this review, we examine the current scientific knowledge on tumor associated CSF miRNAs that could guide diagnosis of different brain cancer types, or could be helpful in predicting disease progression and therapy response. Finally, we highlight their potential applications clinically as biomarkers and discuss limitations.

Quantitative Profiling of Post-translational Modifications by Immunoaffinity Enrichment and LC-MS/MS in Cancer Serum without Immunodepletion

A robust method was developed and optimized for enrichment and quantitative analysis of posttranslational modifications (PTMs) in serum/plasma samples by combining immunoaffinity purification and LC-MS/MS without depletion of abundant proteins. The method was used to survey serum samples of patients with acute myeloid leukemia (AML), breast cancer (BC), and nonsmall cell lung cancer (NSCLC). Peptides were identified from serum samples containing phosphorylation, acetylation, lysine methylation, and arginine methylation. Of the PTMs identified, lysine acetylation (AcK) and arginine mono-methylation (Rme) were more prevalent than other PTMs. Label-free quantitative analysis of AcK and Rme peptides was performed for sera from AML, BC, and NSCLC patients. Several AcK and Rme sites showed distinct abundance distribution patterns across the three cancer types. The identification and quantification of posttranslationally modified peptides in serum samples reported here can be used for patient profiling and biomarker discovery research.

Glycosylation-Based Serum Biomarkers for Cancer Diagnostics and Prognostics

Cancer is the second most common cause of death in developed countries with approximately 14 million newly diagnosed individuals and over 6 million cancer-related deaths in 2012. Many cancers are discovered at a more advanced stage but better survival rates are correlated with earlier detection. Current clinically approved cancer biomarkers are most effective when applied to patients with widespread cancer. Single biomarkers with satisfactory sensitivity and specificity have not been identified for the most common cancers and some biomarkers are ineffective for the detection of early stage cancers. Thus, novel biomarkers with better diagnostic and prognostic performance are required. Aberrant protein glycosylation is well known hallmark of cancer and represents a promising source of potential biomarkers. Glycoproteins enter circulation from tissues or blood cells through active secretion or leakage and patient serum is an attractive option as a source for biomarkers from a clinical and diagnostic perspective. A plethora of technical approaches have been developed to address the challenges of glycosylation structure detection and determination. This review summarises currently utilised glycoprotein biomarkers and novel glycosylation-based biomarkers from the serum glycoproteome under investigation as cancer diagnostics and for monitoring and prognostics and includes details of recent high throughput and other emerging glycoanalytical techniques.